2026/05/16 by Rahul Fernandes, Travis Desell · 1 voice
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Advanced Bandit Algorithms Research #Asset allocation #Efficient frontier #Expected return #Financial market #Key (lock) #Portfolio #Portfolio optimization #Risk and Portfolio Optimization #Risk–return spectrum #Sharpe ratio #Stock Market Forecasting Methods #Transaction cost #cs.LG #q-fin.PM
paper · pdf · doi:10.48550/arxiv.2605.28853
openalex publication_date 2026/05/16 · arxiv published 2026/05/16 · arxiv updated 2026/05/16 · openalex created_date 2026/05/30 · openalex updated_date 2026/07/28
Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Standard predict-then-optimize methods first forecast returns and then solve for weights, compounding prediction errors and often failing under regime shifts. We propose an end-to-end framework that directly optimizes differentiable surrogates of key financial metrics - Sharpe ratio, Omega ratio, Conditional Value-at-Risk (CVaR), and Risk Parity - allowing neural networks to learn portfolio weights via backpropagation. Our expanding-window walk-forward procedure, applied to 50 S&P 500 stocks from 2007 to 2023, incorporates realistic bid-ask spread costs and rebalances quarterly. On the challenging out-of-sample test period (2022-2023), the best model - an AttentionLSTM with the Omega-CVaR-RiskParity loss - achieves an annualized Sharpe of 0.29 and a total compounded return of +7.86%, while the S&P 500 delivers -4.52% total return and an annualized Sharpe of -0.02. This outperforms the S&P 500 by 12.38 percentage points (a relative improvement of over 270%), while keeping tail risk (CVaR) nearly unchanged. The framework consistently outperforms the equal-weight portfolio, S&P 500, and traditional methods (MVP, HRP, NCO), demonstrating that embedding financial objectives directly into model training yields robust, economically meaningful outperformance even in adverse market conditions.